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Python 3 (ipykernel)
Kernel status: Idle Executed 2 cellsElapsed time: 2 seconds
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    [1]:
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    #to ignore warnings
    import warnings
    warnings.filterwarnings('ignore')
    data = pd.read_csv("used_cars_data.csv")
    [3]:
    data.head()
    [3]:
    S.No. Name Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price
    0 0 Maruti Wagon R LXI CNG Mumbai 2010 72000 CNG Manual First 26.6 km/kg 998 CC 58.16 bhp 5.0 NaN 1.75
    1 1 Hyundai Creta 1.6 CRDi SX Option Pune 2015 41000 Diesel Manual First 19.67 kmpl 1582 CC 126.2 bhp 5.0 NaN 12.50
    2 2 Honda Jazz V Chennai 2011 46000 Petrol Manual First 18.2 kmpl 1199 CC 88.7 bhp 5.0 8.61 Lakh 4.50
    3 3 Maruti Ertiga VDI Chennai 2012 87000 Diesel Manual First 20.77 kmpl 1248 CC 88.76 bhp 7.0 NaN 6.00
    4 4 Audi A4 New 2.0 TDI Multitronic Coimbatore 2013 40670 Diesel Automatic Second 15.2 kmpl 1968 CC 140.8 bhp 5.0 NaN 17.74
    [5]:
    data.tail()
    [5]:
    S.No. Name Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price
    7248 7248 Volkswagen Vento Diesel Trendline Hyderabad 2011 89411 Diesel Manual First 20.54 kmpl 1598 CC 103.6 bhp 5.0 NaN NaN
    7249 7249 Volkswagen Polo GT TSI Mumbai 2015 59000 Petrol Automatic First 17.21 kmpl 1197 CC 103.6 bhp 5.0 NaN NaN
    7250 7250 Nissan Micra Diesel XV Kolkata 2012 28000 Diesel Manual First 23.08 kmpl 1461 CC 63.1 bhp 5.0 NaN NaN
    7251 7251 Volkswagen Polo GT TSI Pune 2013 52262 Petrol Automatic Third 17.2 kmpl 1197 CC 103.6 bhp 5.0 NaN NaN
    7252 7252 Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan... Kochi 2014 72443 Diesel Automatic First 10.0 kmpl 2148 CC 170 bhp 5.0 NaN NaN
    [7]:
    data.info()
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 7253 entries, 0 to 7252
    Data columns (total 14 columns):
     #   Column             Non-Null Count  Dtype  
    ---  ------             --------------  -----  
     0   S.No.              7253 non-null   int64  
     1   Name               7253 non-null   object 
     2   Location           7253 non-null   object 
     3   Year               7253 non-null   int64  
     4   Kilometers_Driven  7253 non-null   int64  
     5   Fuel_Type          7253 non-null   object 
     6   Transmission       7253 non-null   object 
     7   Owner_Type         7253 non-null   object 
     8   Mileage            7251 non-null   object 
     9   Engine             7207 non-null   object 
     10  Power              7207 non-null   object 
     11  Seats              7200 non-null   float64
     12  New_Price          1006 non-null   object 
     13  Price              6019 non-null   float64
    dtypes: float64(2), int64(3), object(9)
    memory usage: 793.4+ KB
    
    [9]:
    S.No.                7253
    Name                 2041
    Location               11
    Year                   23
    Kilometers_Driven    3660
    Fuel_Type               5
    Transmission            2
    Owner_Type              4
    Mileage               450
    Engine                150
    Power                 386
    Seats                   9
    New_Price             625
    Price                1373
    dtype: int64
    [11]:
    S.No.                   0
    Name                    0
    Location                0
    Year                    0
    Kilometers_Driven       0
    Fuel_Type               0
    Transmission            0
    Owner_Type              0
    Mileage                 2
    Engine                 46
    Power                  46
    Seats                  53
    New_Price            6247
    Price                1234
    dtype: int64
    [13]:
    S.No.                 0.000000
    Name                  0.000000
    Location              0.000000
    Year                  0.000000
    Kilometers_Driven     0.000000
    Fuel_Type             0.000000
    Transmission          0.000000
    Owner_Type            0.000000
    Mileage               0.027575
    Engine                0.634220
    Power                 0.634220
    Seats                 0.730732
    New_Price            86.129877
    Price                17.013650
    dtype: float64
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 7253 entries, 0 to 7252
    Data columns (total 13 columns):
     #   Column             Non-Null Count  Dtype  
    ---  ------             --------------  -----  
     0   Name               7253 non-null   object 
     1   Location           7253 non-null   object 
     2   Year               7253 non-null   int64  
     3   Kilometers_Driven  7253 non-null   int64  
     4   Fuel_Type          7253 non-null   object 
     5   Transmission       7253 non-null   object 
     6   Owner_Type         7253 non-null   object 
     7   Mileage            7251 non-null   object 
     8   Engine             7207 non-null   object 
     9   Power              7207 non-null   object 
     10  Seats              7200 non-null   float64
     11  New_Price          1006 non-null   object 
     12  Price              6019 non-null   float64
    dtypes: float64(2), int64(2), object(9)
    memory usage: 736.8+ KB
    
    [17]:
    Name Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price Car_Age
    0 Maruti Wagon R LXI CNG Mumbai 2010 72000 CNG Manual First 26.6 km/kg 998 CC 58.16 bhp 5.0 NaN 1.75 15
    1 Hyundai Creta 1.6 CRDi SX Option Pune 2015 41000 Diesel Manual First 19.67 kmpl 1582 CC 126.2 bhp 5.0 NaN 12.50 10
    2 Honda Jazz V Chennai 2011 46000 Petrol Manual First 18.2 kmpl 1199 CC 88.7 bhp 5.0 8.61 Lakh 4.50 14
    3 Maruti Ertiga VDI Chennai 2012 87000 Diesel Manual First 20.77 kmpl 1248 CC 88.76 bhp 7.0 NaN 6.00 13
    4 Audi A4 New 2.0 TDI Multitronic Coimbatore 2013 40670 Diesel Automatic Second 15.2 kmpl 1968 CC 140.8 bhp 5.0 NaN 17.74 12
    [23]:
    Name Brand Model
    0 Maruti Wagon R LXI CNG Maruti WagonR
    1 Hyundai Creta 1.6 CRDi SX Option Hyundai Creta1.6
    2 Honda Jazz V Honda JazzV
    3 Maruti Ertiga VDI Maruti ErtigaVDI
    4 Audi A4 New 2.0 TDI Multitronic Audi A4New
    ... ... ... ...
    7248 Volkswagen Vento Diesel Trendline Volkswagen VentoDiesel
    7249 Volkswagen Polo GT TSI Volkswagen PoloGT
    7250 Nissan Micra Diesel XV Nissan MicraDiesel
    7251 Volkswagen Polo GT TSI Volkswagen PoloGT
    7252 Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan... Mercedes-Benz E-Class2009-2013

    7253 rows × 3 columns

    ['Maruti' 'Hyundai' 'Honda' 'Audi' 'Nissan' 'Toyota' 'Volkswagen' 'Tata'
     'Land' 'Mitsubishi' 'Renault' 'Mercedes-Benz' 'BMW' 'Mahindra' 'Ford'
     'Porsche' 'Datsun' 'Jaguar' 'Volvo' 'Chevrolet' 'Skoda' 'Mini' 'Fiat'
     'Jeep' 'Smart' 'Ambassador' 'Isuzu' 'ISUZU' 'Force' 'Bentley'
     'Lamborghini' 'Hindustan' 'OpelCorsa']
    33
    
    [27]:
    Name Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price Car_Age Brand Model
    13 Land Rover Range Rover 2.2L Pure Delhi 2014 72000 Diesel Automatic First 12.7 kmpl 2179 CC 187.7 bhp 5.0 NaN 27.00 11 Land RoverRange
    14 Land Rover Freelander 2 TD4 SE Pune 2012 85000 Diesel Automatic Second 0.0 kmpl 2179 CC 115 bhp 5.0 NaN 17.50 13 Land RoverFreelander
    176 Mini Countryman Cooper D Jaipur 2017 8525 Diesel Automatic Second 16.6 kmpl 1998 CC 112 bhp 5.0 NaN 23.00 8 Mini CountrymanCooper
    191 Land Rover Range Rover 2.2L Dynamic Coimbatore 2018 36091 Diesel Automatic First 12.7 kmpl 2179 CC 187.7 bhp 5.0 NaN 55.76 7 Land RoverRange
    228 Mini Cooper Convertible S Kochi 2017 26327 Petrol Automatic First 16.82 kmpl 1998 CC 189.08 bhp 4.0 44.28 Lakh 35.67 8 Mini CooperConvertible
    [31]:
    count mean std min 25% 50% 75% max
    Year 7253.0 2013.365366 3.254421 1996.00 2011.0 2014.00 2016.00 2019.0
    Kilometers_Driven 7253.0 58699.063146 84427.720583 171.00 34000.0 53416.00 73000.00 6500000.0
    Seats 7200.0 5.279722 0.811660 0.00 5.0 5.00 5.00 10.0
    Price 6019.0 9.479468 11.187917 0.44 3.5 5.64 9.95 160.0
    Car_Age 7253.0 11.634634 3.254421 6.00 9.0 11.00 14.00 29.0
    [33]:
    count unique top freq mean std min 25% 50% 75% max
    Name 7253 2041 Mahindra XUV500 W8 2WD 55 NaN NaN NaN NaN NaN NaN NaN
    Location 7253 11 Mumbai 949 NaN NaN NaN NaN NaN NaN NaN
    Year 7253.0 NaN NaN NaN 2013.365366 3.254421 1996.0 2011.0 2014.0 2016.0 2019.0
    Kilometers_Driven 7253.0 NaN NaN NaN 58699.063146 84427.720583 171.0 34000.0 53416.0 73000.0 6500000.0
    Fuel_Type 7253 5 Diesel 3852 NaN NaN NaN NaN NaN NaN NaN
    Transmission 7253 2 Manual 5204 NaN NaN NaN NaN NaN NaN NaN
    Owner_Type 7253 4 First 5952 NaN NaN NaN NaN NaN NaN NaN
    Mileage 7251 450 17.0 kmpl 207 NaN NaN NaN NaN NaN NaN NaN
    Engine 7207 150 1197 CC 732 NaN NaN NaN NaN NaN NaN NaN
    Power 7207 386 74 bhp 280 NaN NaN NaN NaN NaN NaN NaN
    Seats 7200.0 NaN NaN NaN 5.279722 0.81166 0.0 5.0 5.0 5.0 10.0
    New_Price 1006 625 63.71 Lakh 6 NaN NaN NaN NaN NaN NaN NaN
    Price 6019.0 NaN NaN NaN 9.479468 11.187917 0.44 3.5 5.64 9.95 160.0
    Car_Age 7253.0 NaN NaN NaN 11.634634 3.254421 6.0 9.0 11.0 14.0 29.0
    Brand 7253 32 Maruti 1444 NaN NaN NaN NaN NaN NaN NaN
    Model 7252 726 SwiftDzire 189 NaN NaN NaN NaN NaN NaN NaN
    Categorical Variables:
    Index(['Name', 'Location', 'Fuel_Type', 'Transmission', 'Owner_Type',
           'Mileage', 'Engine', 'Power', 'New_Price', 'Brand', 'Model'],
          dtype='object')
    Numerical Variables:
    ['Year', 'Kilometers_Driven', 'Seats', 'Price', 'Car_Age']
    
    Year
    Skew : -0.84
    
    Kilometers_Driven
    Skew : 61.58
    
    Seats
    Skew : 1.9
    
    Price
    Skew : 3.34
    
    Car_Age
    Skew : 0.84
    
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 7253 entries, 0 to 7252
    Data columns (total 18 columns):
     #   Column                 Non-Null Count  Dtype  
    ---  ------                 --------------  -----  
     0   Name                   7253 non-null   object 
     1   Location               7253 non-null   object 
     2   Year                   7253 non-null   int64  
     3   Kilometers_Driven      7253 non-null   int64  
     4   Fuel_Type              7253 non-null   object 
     5   Transmission           7253 non-null   object 
     6   Owner_Type             7253 non-null   object 
     7   Mileage                7251 non-null   object 
     8   Engine                 7207 non-null   object 
     9   Power                  7207 non-null   object 
     10  Seats                  7200 non-null   float64
     11  New_Price              1006 non-null   object 
     12  Price                  6019 non-null   float64
     13  Car_Age                7253 non-null   int64  
     14  Brand                  7253 non-null   object 
     15  Model                  7252 non-null   object 
     16  Kilometers_Driven_log  7253 non-null   float64
     17  Price_log              6019 non-null   float64
    dtypes: float64(4), int64(3), object(11)
    memory usage: 1020.1+ KB
    
    [47]:
    plt.figure(figsize=(13,17))
    sns.pairplot(data=data.drop(['Kilometers_Driven','Price'],axis=1))
    plt.show()
    <Figure size 1300x1700 with 0 Axes>
    [53]:
    2
    [57]:

    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    File ~\anaconda3\Lib\site-packages\pandas\core\frame.py:12687, in _reindex_for_setitem(value, index)
      12686 try:
    > 12687     reindexed_value = value.reindex(index)._values
      12688 except ValueError as err:
      12689     # raised in MultiIndex.from_tuples, see test_insert_error_msmgs
    
    File ~\anaconda3\Lib\site-packages\pandas\core\series.py:5153, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5136 @doc(
       5137     NDFrame.reindex,  # type: ignore[has-type]
       5138     klass=_shared_doc_kwargs["klass"],
       (...)
       5151     tolerance=None,
       5152 ) -> Series:
    -> 5153     return super().reindex(
       5154         index=index,
       5155         method=method,
       5156         copy=copy,
       5157         level=level,
       5158         fill_value=fill_value,
       5159         limit=limit,
       5160         tolerance=tolerance,
       5161     )
    
    File ~\anaconda3\Lib\site-packages\pandas\core\generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5609 # perform the reindex on the axes
    -> 5610 return self._reindex_axes(
       5611     axes, level, limit, tolerance, method, fill_value, copy
       5612 ).__finalize__(self, method="reindex")
    
    File ~\anaconda3\Lib\site-packages\pandas\core\generic.py:5633, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
       5632 ax = self._get_axis(a)
    -> 5633 new_index, indexer = ax.reindex(
       5634     labels, level=level, limit=limit, tolerance=tolerance, method=method
       5635 )
       5637 axis = self._get_axis_number(a)
    
    File ~\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:4433, in Index.reindex(self, target, method, level, limit, tolerance)
       4431             indexer, _ = self.get_indexer_non_unique(target)
    -> 4433 target = self._wrap_reindex_result(target, indexer, preserve_names)
       4434 return target, indexer
    
    File ~\anaconda3\Lib\site-packages\pandas\core\indexes\multi.py:2717, in MultiIndex._wrap_reindex_result(self, target, indexer, preserve_names)
       2716 try:
    -> 2717     target = MultiIndex.from_tuples(target)
       2718 except TypeError:
       2719     # not all tuples, see test_constructor_dict_multiindex_reindex_flat
    
    File ~\anaconda3\Lib\site-packages\pandas\core\indexes\multi.py:222, in names_compat.<locals>.new_meth(self_or_cls, *args, **kwargs)
        220     kwargs["names"] = kwargs.pop("name")
    --> 222 return meth(self_or_cls, *args, **kwargs)
    
    File ~\anaconda3\Lib\site-packages\pandas\core\indexes\multi.py:617, in MultiIndex.from_tuples(cls, tuples, sortorder, names)
        615         tuples = np.asarray(tuples._values)
    --> 617     arrays = list(lib.tuples_to_object_array(tuples).T)
        618 elif isinstance(tuples, list):
    
    File lib.pyx:3029, in pandas._libs.lib.tuples_to_object_array()
    
    ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long long'
    
    The above exception was the direct cause of the following exception:
    
    TypeError                                 Traceback (most recent call last)
    ~\AppData\Local\Temp\ipykernel_6964\2680079267.py in ?()
          1 data.Seats.isnull().sum()
          2 data['Seats'].fillna(value=np.nan,inplace=True)
    ----> 3 data['Seats']=data.groupby(['Model','Brand'])['Seats'].apply(lambda x:x.fillna(x.median()))
          4 data['Engine']=data.groupby(['Brand','Model'])['Engine'].apply(lambda x:x.fillna(x.median()))
          5 data['Power']=data.groupby(['Brand','Model'])['Power'].apply(lambda x:x.fillna(x.median()))
    
    ~\anaconda3\Lib\site-packages\pandas\core\frame.py in ?(self, key, value)
       4307             # Column to set is duplicated
       4308             self._setitem_array([key], value)
       4309         else:
       4310             # set column
    -> 4311             self._set_item(key, value)
    
    ~\anaconda3\Lib\site-packages\pandas\core\frame.py in ?(self, key, value)
       4520 
       4521         Series/TimeSeries will be conformed to the DataFrames index to
       4522         ensure homogeneity.
       4523         """
    -> 4524         value, refs = self._sanitize_column(value)
       4525 
       4526         if (
       4527             key in self.columns
    
    ~\anaconda3\Lib\site-packages\pandas\core\frame.py in ?(self, value)
       5259         assert not isinstance(value, DataFrame)
       5260         if is_dict_like(value):
       5261             if not isinstance(value, Series):
       5262                 value = Series(value)
    -> 5263             return _reindex_for_setitem(value, self.index)
       5264 
       5265         if is_list_like(value):
       5266             com.require_length_match(value, self.index)
    
    ~\anaconda3\Lib\site-packages\pandas\core\frame.py in ?(value, index)
      12690         if not value.index.is_unique:
      12691             # duplicate axis
      12692             raise err
      12693 
    > 12694         raise TypeError(
      12695             "incompatible index of inserted column with frame index"
      12696         ) from err
      12697     return reindexed_value, None
    
    TypeError: incompatible index of inserted column with frame index

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        Set the terminal theme
      • Use Terminal Theme: Inherit
        Set the terminal theme
      • Use Terminal Theme: Light
        Set the terminal theme
      • Text Editor
      • Decrease Font Size
      • Increase Font Size
      • New Markdown File
        Create a new markdown file
      • New Python File
        Create a new Python file
      • New Text File
        Create a new text file
      • Spaces: 1
      • Spaces: 2
      • Spaces: 4
      • Spaces: 4
      • Spaces: 8
      • Theme
      • Decrease Code Font Size
      • Decrease Content Font Size
      • Decrease UI Font Size
      • Increase Code Font Size
      • Increase Content Font Size
      • Increase UI Font Size
      • Set Preferred Dark Theme: JupyterLab Dark
      • Set Preferred Dark Theme: JupyterLab Dark High Contrast
      • Set Preferred Dark Theme: JupyterLab Light
      • Set Preferred Light Theme: JupyterLab Dark
      • Set Preferred Light Theme: JupyterLab Dark High Contrast
      • Set Preferred Light Theme: JupyterLab Light
      • Synchronize Styling Theme with System Settings
      • Theme Scrollbars
      • Use Theme: JupyterLab Dark
      • Use Theme: JupyterLab Dark High Contrast
      • Use Theme: JupyterLab Light
      • View
      • File Browser
      • Open JupyterLab
      • Show Anaconda Assistant
        Show Show Anaconda Assistant in the right sidebar
      • Show Debugger
        Show Show Debugger in the right sidebar
      • Show Header
      • Show Notebook Tools
        Show Show Notebook Tools in the right sidebar
      • Show Table of Contents
        Show Show Table of Contents in the left sidebar